Using this result 1 \u03b1 prediction interval for a new observation Y h new is \u02c6 Y

# Using this result 1 α prediction interval for a new

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Using this result, (1 - α ) prediction interval for a new observation Y h ( new ) is ˆ Y h t 1 - α/ 2 ,n - 2 se pred . 14
2.4.5 Inference about both β 0 and β 1 simultaneously Suppose that β * 0 and β * 1 are given numbers and we are interested in testing the following hypothesis: H 0 : β 0 = β * 0 and β 1 = β * 1 versus H 1 : at least one is different (9) We shall derive the likelihood ratio test for (9). The likelihood function (7), when maximized under the unconstrained space yields the MLEs ˆ β 1 , ˆ β 1 , ˆ σ 2 . Under the constrained space, β 0 and β 1 are fixed at β * 0 and β * 1 , and so ˆ σ 2 0 = 1 n n X i =1 ( Y i - β * 0 - β * 1 x i ) 2 . The likelihood statistic reduces to Λ( Y , x ) = sup σ 2 L ( β * 0 , β * 1 , σ 2 ) sup β 0 1 2 L ( β 0 , β 1 , σ 2 ) = ˆ σ 2 ˆ σ 2 0 n/ 2 = " n i =1 ( Y i - ˆ β 0 - ˆ β 1 x i ) 2 n i =1 ( Y i - β * 0 - β * 1 x i ) 2 # n/ 2 . The LRT procedure specifies rejecting H 0 when Λ( Y , x ) k, for some k , chosen given the level condition. Exercise: Show that n X i =1 ( Y i - β * 0 - β * 1 x i ) 2 = S 2 + Q 2 , where S 2 = n X i =1 ( Y i - ˆ β 0 - ˆ β 1 x i ) 2 Q 2 = n ( ˆ β 0 - β * 0 ) 2 + n X i =1 x 2 i ! ( ˆ β 1 - β * 1 ) 2 + 2 n ¯ x ( ˆ β 0 - β * 0 )( ˆ β 1 - β * 1 ) . Thus, Λ( Y , x ) = S 2 S 2 + Q 2 n/ 2 = 1 + Q 2 S 2 - n/ 2 . It can be seen that this is equivalent to rejecting H 0 when Q 2 /S 2 k 0 which is equivalent to U 2 := 1 2 Q 2 ˜ σ 2 γ. 15
Exercise: Show that, under H 0 , Q 2 σ 2 χ 2 2 . Also show that Q 2 and S 2 are independent. We know that S 2 2 χ 2 n - 2 . Thus, under H 0 , U 2 F 2 ,n - 2 , and thus γ = F - 1 2 ,n - 2 (1 - α ). 3 Linear models with normal errors 3.1 Basic theory This section concerns models for independent responses of the form Y i N ( μ i , σ 2 ) , where μ i = x > i β for some known vector of explanatory variables x > i = ( x i 1 , . . . , x ip ) and unknown parameter vector β = ( β 1 , . . . , β p ) > , where p < n . This is the linear model and is usually written as Y = X β + ε (in vector notation) where Y n × 1 = Y 1 . . . Y n , X n × p = x > 1 . . . x > n , β p × 1 = β 1 . . . β p , ε n × 1 = ε 1 . . . ε n , ε i i.i.d. N (0 , σ 2 ) . Sometimes this is written in the more compact notation Y N n ( X β , σ 2 I ) , where I is the n × n identity matrix. It is usual to assume that the n × p matrix X has full rank p . 16
3.2 Maximum likelihood estimation The log–likelihood (up to a constant term) for ( β , σ 2 ) is ( β , σ 2 ) = - n 2 log σ 2 - 1 2 σ 2 n X i =1 ( Y i - x > i β ) 2 = - n 2 log σ 2 - 1 2 σ 2 n X i =1 Y i - p X j =1 x ij β j ! 2 . An MLE ( ˆ β , ˆ σ 2 ) satisfies 0 = ∂β j ( ˆ β , ˆ σ 2 ) = 1 ˆ σ 2 n X i =1 x ij ( y i - x > i ˆ β ) , for j = 1 , . . . , p, i.e., n X i =1 x ij x > i ˆ β = n X i =1 x ij y i for j = 1 , . . . , p, so ( X > X ) ˆ β = X > Y . Since X > X is non-singular if X has rank p , we have ˆ β = ( X > X ) - 1 X > Y . The least squares estimator of β minimizes k Y - X β k 2 . Check that this estimator coincides with the MLE when the errors are normally distributed.

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